According to the National Aphasia Association , about 2 million U.S. citizens live with aphasia, but only two-thirds of Americans are aware of the condition, which is a communication disorder that often occurs after a stroke. It can affect many different neurophysiological processes relating to communication, such as reading, speaking, or gesturing. June is National Aphasia Awareness Month, and BU's Aphasia resource center helped advocate for Massachusetts Gov. Maura Healy to issue a proclamation recognizing it.
For bilingual people, aphasia presents additional challenges than those who are monolingual, because "when someone knows more than one language, these languages share resources within the brain, but they also have individual and separate control mechanisms" , making the approach to rehabilitation more difficult. Some people may have differences in how aphasia affects one language over the other or the speed at which one language recovers from a stroke, for example.
Swathi Kiran is the Founding Director of the Center for Brain Recovery (CBR) at Boston University, and her work is focused on neuroscience, brain plasticity, language recovery, and bilingualism. She and her team publish over 30 papers each year.
In a study published in Nature , Dr. Kiran utilized an artificial intelligence model to predict which language for bilingual aphasia patients would be most effective for recovery, based upon the patient's "digital twin". In a Q&A, Dr. Kiran explains what aphasia is, discusses the recent research findings, and explains how the use of artificial intelligence may help guide rehabilitation decisions in bilingual patients.
Boston University: Your study looked at people who have aphasia, could you briefly explain what aphasia is and why it can be difficult to treat, especially for those that speak multiple languages?
Swathi Kiran: The difficulty or inability to speak or understand language is called aphasia. Aphasia usually occurs to a person due to a stroke or a brain injury. The most common type of stroke is an ischemic stroke, when a vessel supplying blood to the brain is obstructed. Typically a stroke occurs in the left middle cerebral artery, which supplies blood to parts of the brain involved in speech and language. In multilingual individuals with aphasia, impairment typically affects all languages rather than just one, possibly because stroke disrupts the network that enables switching between languages.
BU: This study used an AI model called BiLex to create "digital twins" of patients, what is it, and what makes it different from the AI tools most people are familiar with?
Kiran: BiLex is an AI system designed to act like a "digital twin"—a detailed, computer-based copy of an individual patient's language system in the brain. Instead of just analyzing data, it tries to simulate how that person's brain organizes and uses words across languages, based on their lifetime language experience and their specific impairment after stroke. Bilex is a brain-inspired model of language, with separate systems for each language and shared meaning representations. It is personalized as it is trained to match one individual's language history and their language difficulties after the stroke. And it can be "experimented on" safely—researchers can simulate brain damage and try different therapies to see what works best for that specific patient. It is different from AI tools that most people are familiar with in that it is not just pattern recognition from large data sets. Typical AI generates answers or images, but digital twin models like BiLex helps researchers understand why language breaks down and how it can recover.
BU: Before this study, how did clinicians decide which language a bilingual aphasia patient should focus on in therapy?
Kiran: Before this study, clinicians usually decide which language a bilingual aphasia patient should focus on by (a) asking the patient what language they want to focus on and provide therapy in that language whether or not that was an optimal language, or (b) if they did not speak the languages the patient spoke, they simply provided therapy in English, whether or not that was an optimal language.
BU: What happened when patients followed the language the model recommended versus when they didn't?
Kiran: In this study, we made two observations. First, when simply looking at group-level differences between the patients who followed the model prescription versus the placebo, there were no significant differences between the groups. However, when we subcategorized the patients into smaller cohorts organized by language history or how severe their aphasia was, we found that the digital twin simulations accurately captured these differences between people. In other words, the digital twin was able to capture differences in an individual's language profiles accurately.
BU: What are the next steps in this research? Are there areas that need to be explored further before your findings can be put into practice?
Kiran: These results really showed us that bilingual aphasia recovery is complex and demonstrates the potential of computational models to guide rehabilitation strategies. As the field of computational modeling advances, these tools will become increasingly valuable for developing personalized therapy plans that account for the unique linguistic profiles of patients.
